Cognitive-based passenger selection

ABSTRACT

Systems, methods, and computer program products to perform an operation comprising receiving a plurality of rules associated with a first seat in a mass-transit vehicle, determining a plurality of physical attributes for a first passenger, of a plurality of passengers, based on data describing the first passenger received from a plurality of data sources, determining a plurality of non-physical attributes for the first passenger based on the received data describing the first passenger, determining that at least one of the plurality of physical attributes and the plurality of non-physical attributes of the first passenger satisfy each rule in the set of rules associated with the first seat, computing, based on a machine learning (ML) model a score for the first passenger, determining that the score exceeds a threshold score associated with the first seat, and allocating the first passenger to the first seat in the mass-transit vehicle.

BACKGROUND

The present invention relates to computer software, and morespecifically, to computer software which provides cognitive-basedpassenger selection for vehicles.

Mass transit operators are constantly trying to improve safety measuresand precautions used to ensure the safety of all people on board.However, limited attention has been given to programmatically allocatingpassengers to seats, especially seats that are associated with variousrestrictions, such as exit-row seats in an aircraft, train, or bus.Often, users select these seats because of the extra leg room provided.However, many users select these seats even if they are unwilling and/orunable to perform the duties associated with the seats. In somecircumstances, mass transit employees may allocate passengers to seatsbefore the vehicle departs. In such circumstances, the employees may notbe able to determine whether a passenger is willing and/or able toperform the duties associated with the seats. As such, the safety of allpersons on board may be at risk during an emergency situation.

SUMMARY

In one embodiment, a method comprises receiving a plurality of rulesassociated with a first seat in a mass-transit vehicle, determining aplurality of physical attributes for a first passenger, of a pluralityof passengers, based on data describing the first passenger receivedfrom a plurality of data sources, determining a plurality ofnon-physical attributes for the first passenger based on the receiveddata describing the first passenger, determining that at least one ofthe plurality of physical attributes and the plurality of non-physicalattributes of the first passenger satisfy each rule in the set of rulesassociated with the first seat, computing, based on a machine learning(ML) model a score for the first passenger, determining that the scoreexceeds a threshold score associated with the first seat, and allocatingthe first passenger to the first seat in the mass-transit vehicle.

In another embodiment, a system comprises a processor and a memorystoring instructions, which when executed by the processor, performs anoperation comprising receiving a plurality of rules associated with afirst seat in a mass-transit vehicle, determining a plurality ofphysical attributes for a first passenger, of a plurality of passengers,based on data describing the first passenger received from a pluralityof data sources, determining a plurality of non-physical attributes forthe first passenger based on the received data describing the firstpassenger, determining that at least one of the plurality of physicalattributes and the plurality of non-physical attributes of the firstpassenger satisfy each rule in the set of rules associated with thefirst seat, computing, based on a machine learning (ML) model a scorefor the first passenger, determining that the score exceeds a thresholdscore associated with the first seat, and allocating the first passengerto the first seat in the mass-transit vehicle.

In another embodiment, a non-transitory computer-readable storage mediumhas computer-readable program code embodied therewith, thecomputer-readable program code executable by a processor to perform anoperation comprising receiving a plurality of rules associated with afirst seat in a mass-transit vehicle, determining a plurality ofphysical attributes for a first passenger, of a plurality of passengers,based on data describing the first passenger received from a pluralityof data sources, determining a plurality of non-physical attributes forthe first passenger based on the received data describing the firstpassenger, determining that at least one of the plurality of physicalattributes and the plurality of non-physical attributes of the firstpassenger satisfy each rule in the set of rules associated with thefirst seat, computing, based on a machine learning (ML) model a scorefor the first passenger, determining that the score exceeds a thresholdscore associated with the first seat, and allocating the first passengerto the first seat in the mass-transit vehicle.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 illustrates an example system architecture which providescognitive-based passenger selection for vehicles, according to oneembodiment.

FIG. 2 is a schematic illustrating an example cognitive-based selectionof passengers, according to one embodiment.

FIG. 3 is a flow chart illustrating an example method to providecognitive-based passenger selection for vehicles, according to oneembodiment.

FIG. 4 is a flow chart illustrating an example method to determinephysical attributes of passengers, according to one embodiment.

FIG. 5 is a flow chart illustrating an example method to determinenon-physical attributes of passengers, according to one embodiment.

FIG. 6 is a flow chart illustrating an example method to allocatepassengers to seats in a vehicle based on ordered list of candidatepassengers, according to one embodiment.

FIG. 7 illustrates an example system which provides cognitive-basedpassenger selection for vehicles, according to one embodiment.

DETAILED DESCRIPTION

Embodiments disclosed herein leverage cognitive computing tointelligently allocate passengers to seats in a vehicle in accordancewith predefined rules associated with the seats. Generally, embodimentsdisclosed herein identify different physical and non-physical attributesof each passenger. The attributes may include, without limitation,behavioral, cognitive, mental, emotional, personal, and physicalattributes. Embodiments disclosed herein leverage the identifiedattributes to generate an allocation of passengers to seats that ensuresthe rules associated with each seat are satisfied by each user allocatedto the seat. For example, a person may be physically fit, but unable toact in stressful situations due to fear of heights. Therefore,embodiments disclosed herein would refrain from assigning the person toan exit row seat in an aircraft. Instead, embodiments disclosed hereinwould attempt to identify persons who are physically, mentally, andemotionally able to sit in the exit row seat in the aircraft.

FIG. 1 illustrates an example system architecture 100 which providescognitive-based passenger selection for vehicles, according to oneembodiment. As shown, the system 100 includes a server 101, one or moreinventory sources 102, a plurality of services 103, and a plurality ofdata sources 104 communicably coupled via a network 130. The server 101includes an allocation engine 105, and data stores of machine learning(ML) models 106, rules 107, allocation data 108, and training data 109.The allocation engine 105 is configured to intelligently allocatepassengers to seats in vehicles based on programmatically determinedattributes of the passengers and one or more rules in the rules 107applicable to each seat in a given vehicle. The passenger attributesinclude, without limitation, physical attributes and non-physicalattributes, such as mental attributes, emotional attributes, personalitytraits, preferences, and the like. The vehicles include any type ofpassenger vehicle, such as airplanes, trains, and buses. Althoughairplanes, trains, and/or buses are used as reference examples herein,the techniques of the disclosure are applicable to any type of masstransit vehicle.

The rules 107 are representative of any type of rule, such as rulesspecifying required physical attributes of a passenger to sit in a givenseat in a vehicle, such as minimum and/or maximum passenger heightand/or weight, passenger ability to lift a minimum amount of weight,minimum muscle mass of a passenger, no current injuries, etc. The rules107 further include rules specifying required mental attributes,emotional attributes, personality traits, and other non-physicalattributes (e.g., age) required to sit in a given seat in a vehicle. Forexample, a rule in the rules 107 may specify that passengers with fearof heights cannot sit in an exit-row seat in an aircraft. As anotherexample, a rule in the rules 107 may specify that passengers must beable to perform tasks in emergency situations. Generally, the rules 107include rules defined by law, as well as rules defined by a systemadministrator (e.g., the operator of an airline, train, etc.) to enhancepassenger safety.

The allocation engine 105 generates the ML models 106 during a trainingsession based on the training data 109. The training data 109 includeslabeled input that allows the allocation engine 105 to compute a desiredoutcome. For example, the training data 109 may include a set of inputparameters related to different types of passenger attributes and rules107 associated with a plurality of different types of seats in differenttypes of vehicles. The training data 109 may further specify whether oneor more passenger attributes would satisfy one or more rules 107 for agiven seat in a given type of vehicle. Therefore, as part of thetraining session, the allocation engine 105 generates the ML models 106that specify features and associated weights. The allocation engine 105then uses the ML models 106 to allocate passengers to seats in vehicles.For example, during training, the allocation engine 105 may determinethat a range of body mass index (BMI) values is correlated withpassengers who satisfy the rules 107 for a seat in a vehicle. In such anexample, the allocation engine 105 generates an ML model 106 whichspecifies a greater weight to passengers having a BMI in the determinedrange of BMI values. As another example, the allocation engine 105 maydetermine that passengers who engage in action sports (e.g., skydiving)are correlated with passengers who satisfy the rules 107 for a seat in avehicle. In such an example, the allocation engine 105 generates an MLmodel 106 which specifies a greater weight to those passengers whoengage in action sports. In both examples, doing so allows theallocation engine 105 to allocate passengers to seats in a manner whichsatisfies the rules 107 more to a degree that is greater than allocatingpassengers to seats without using the ML models 106.

Once trained, the allocation engine 105 may receive requests to allocatepassengers to seats, and generate allocations which are stored in theallocation data 108. For example, the allocation engine 105 may receivea request from an airline, and receive inventory data 110 from theinventory source 102 for the airline. The inventory sources 102 arerepresentative of mass transit data systems, such as airline datasystems, train data systems, railroad data systems, etc. The inventorydata 110 specifies passengers who have tickets for a given trip in avehicle, the available seats in the vehicle, and one or more attributesfor each seat in the vehicle. The attributes for the seat may includeany associated rules for the seat as well as a priority for the seat.The seat priority may reflect a relative level of importance allocatingpassengers to the seats who meet predefined requirements in order toimprove safety for the passengers in the vehicle. The inventory data 110may further include user-specified data (e.g., information providedduring the ticketing process), which may include any number and type ofphysical and non-physical attributes.

To generate the allocations of passengers to seats, the allocationengine 105 processes information regarding each passenger specified inthe inventory data 110. More specifically, the allocation engine 105receives data describing each passenger from one or more data sources104. The data sources 104 are representative of any type of data source.As shown, the data sources 104 include, without limitation, social mediadata 113, publication data 114, and profiles 115. The social media data113 is representative of data from user accounts on social mediaplatforms, where users may specify attributes as biographicalinformation, friendships, relationships, and the like. Similarly, thesocial media data 113 is representative of photos, videos, text, andother types of content posted by the user on the social media platforms.The publication data 114 is representative of blogs, articles, and anyother type of information published by the users on the Internet. Theprofiles 115 are representative of any type of user profile which storesdata describing user attributes.

For example, a user may maintain a travel blog. The allocation engine105 may receive the publication data 114 from the user's travel blog,and perform natural language processing on the text of the blog toextract different attributes of the user therefrom. For example, theallocation engine 105 may determine that the user enjoys to travel, andfrequently engages in surfing and skiing. Similarly, the allocationengine 105 may determine that the user is in several groups related totravel and action sports on one or more social media platforms.

The allocation engine 105 further leverages the services 103, which arerepresentative of any type of online service. As shown, the services 103include, without limitation, the non-physical attribute service 111 andthe physical attribute service 112. The non-physical attribute service111 is generally configured to discover information describingnon-physical attributes of a person. The non-physical attributesinclude, without limitation, how the person acts, how the person thinks,emotions expressed by the person, mental health, preferences, likes,dislikes, personality traits, and the like. One example of anon-physical attribute service 111 is “Watson Personality Insights” byIBM Corporation of Armonk, N.Y. The physical attribute service 112extracts physical attributes of a given person based on an analysis ofone or more images of the person. For example, the physical attributeservice 112 may apply image analysis algorithms to detect attributes ofthe person(s) depicted in an image. The image analysis algorithms mayinclude computer vision algorithms (e.g., to detect objects, features,or other physical attributes), facial recognition algorithms, and otherimage analysis routines. Doing so allows the physical attribute service112 to determine an approximate, height, weight, BMI, amount of musclemass, etc. of a person. Similarly, the physical attribute service 112may identify whether the person is depicted in images engaging in sportsor other activities. Furthermore, the physical attribute service 112 mayidentify whether the person has limitations (e.g., is wearing a cast toheal a broken bone). One example of a physical attribute service 112 is“Watson Visual Recognition” by IBM.

Therefore, for each passenger, the allocation engine 105 may make anapplication programming interface (API) call to the non-physicalattribute service 111, which specifies to extract non-physicalattributes of the passenger (e.g., based on the data from the datasources 104). Similarly, the allocation engine 105 makes an API call tothe physical attribute service 112 specifying to extract physicalattributes of each passenger based on the data from the data sources104. The allocation engine 105 uses the results provided by thenon-physical attribute service 111 and the physical attribute service112 to allocate passengers to seats. For example, if the physicalattribute service 112 identifies an image of an example passenger inwhich the passenger is wearing a cast on their arm, the allocationengine 105 may compute a score for the passenger (based on the ML models106) which indicates the user should not be seated in an exit row seatof an aircraft. As another example, if the non-physical attributeservice 111 returns an indication that a passenger enjoys to travel andis engaged in action sports, the allocation engine 105 may compute ascore which indicates the user is able to sit in an exit row seat of anaircraft.

More generally, if at any point the allocation engine 105 determinesthat an attribute of the person violates a rule in the rules 107 for agiven seat, the allocation engine 105 refrains from allocating thatperson to the seat. For example, if a profile 115 specifies that theperson's age is outside of a range of ages specified in the rules 107for sitting in a given seat, the allocation engine 105 refrains fromallocating the person to the seat. Similarly, if a person has a brokenbone, a rule in the rules 107 may be violated for an exit row seat, andthe allocation engine 105 would refrain from allocating the person to anexit row seat.

The allocation engine 105 may be invoked at any time to refine the seatallocations in the allocation data 108. For example, the allocationengine 105 may initially allocate a person Y to an exit row seat.However, before the flight departs, the allocation engine 105 mayreprocess the data of person Y, and determine that person Y published ablog post (after the allocation of person Y to the exit row seat)expressing a fear of flying. As such, the allocation engine 105 mayremove person Y from the exit row seat, and select a more suitableperson for the exit row seat.

FIG. 2 is a schematic 200 illustrating an example cognitive-basedselection of passengers, according to one embodiment. As shown, FIG. 2depicts an example vehicle 201 that includes example seats 202-213.Although the vehicle 201 is shown as including the seats 202-213, thevehicle 201 may generally include any number of seats. In the exampledepicted in FIG. 2, seats 206-209 may be “exit row” seats that includeone or more rules defined in the rules 107. For example, the rules 107may specify rules related to physical attributes such as height, weight,lifting ability, BMI, etc. The rules 107 may further specify rulesrelated to non-physical attributes, such as not being scared of heights,willingness (or ability) to assist others, good mental health, emotionsthat cannot be expressed (e.g., fear, hostility, etc.), and emotionsthat must be expressed (e.g., confidence, assertiveness, etc.). Therules 107 may further specify minimum score thresholds for sitting in agiven seat.

As shown, FIG. 2 further depicts example service results 220-223returned by the services 111, 112 for four example persons. However, insome embodiments, the allocation engine 105 may make similardeterminations based on the data received from the data sources 104 fora given person. Based on the service results 220-223, the allocationengine 105 computes one or more scores for the respective person usingthe weights defined in the ML models 106. For example, the allocationengine 105 has computed a score of 99 (on a scale of 0-100) for personA, based on the service results 220 specifying that person A isphysically fit, authors an adventure sports blog, and is generallyexcited about travel. As another example, the allocation engine 105 hascomputed a score of 25 for person B based on the results 221, whichspecify that person B is currently wearing a cast on their leg (whichmay violate a rule in the rules 107), is not physically fit, but enjoysflying and is willing to help others.

Once the allocation engine 105 computes a score for each person, theallocation engine 105 generates a list that ranks each person based onthe computed score, and allocates the people to the seats 202-213 basedon the rankings. For example, as shown, FIG. 2 depicts an example tableof allocation data 108, where the allocation engine 105 has allocatedperson A to seat 206, person B to seat 211, person C to seat 204, andperson D to seat 209. Therefore, the allocation engine 105 allocates thepeople with the highest scores to the exit row seats 206, 209, whilerefraining from allocating people with lower scores to seats 207-208.The allocation engine 105 may refrain from allocating the people withlower scores to seats 207-208 based on a determination that one of therules 107 has been violated and/or based on a determination that thescores computed for persons B, C in results 221, 222 fall below athreshold score for sitting in seats 206-209. Similarly, the seats202-205 and 210-213 may have associated rules 107 and/or scorethresholds. For example, seat 204 may have additional leg room, and arule 107 specifies that seat 202 should be allocated to users needingthe additional leg room. Therefore, the allocation engine 105determines, based on person B wearing a cast on their leg, that person Bsatisfies this rule, and allocates person B to seat 204.

FIG. 3 is a flow chart illustrating an example method 300 to providecognitive-based passenger selection for vehicles, according to oneembodiment. As shown, the method 300 begins at block 310, where theallocation engine 105 is trained to produce the ML models 106 asdescribed above. At block 320, the allocation engine 105 receivesinventory data 110 for a given vehicle as part of a request to generatean allocation of passengers to seats in the vehicle for a given trip (orplurality of trips) of the vehicle. At block 330, the allocation engine105 receives the passenger data for the vehicle, which may be specifiedin the inventory data 110 received at block 320. Generally, thepassenger data includes an indication of each passenger who currentlyholds a ticket to travel on the vehicle. At block 340, the allocationengine 105 filters the passengers using a first set of rules in therules 107. For example, if a first passenger does not meet heightrequirements mandated by law for an exit row seat, the allocation engine105 removes the first passenger from consideration for allocation toexit row seats.

At block 350, described in greater detail below with reference to FIG.4, the allocation engine 105 determines the physical attributes of eachremaining passenger that was not filtered at block 340. Generally, theallocation engine 105 leverages the data sources 104 and the physicalattribute service 112 to determine the physical attributes of a givenpassenger. For example, the allocation engine 105 may determine that aperson has significant muscle mass, is generally physically fit, andmeets all height and weight requirements for an exit row seat specifiedin the rules 107. The allocation engine 105 may then compute a score forthe user reflecting a high degree of suitability for allocating theperson to an exit row seat on an aircraft. At block 360, described ingreater detail below with reference to FIG. 5, the allocation engine 105determines non-physical attributes of each remaining passenger that wasnot filtered at block 340. As previously stated, the non-physicalattributes include emotions, metal state, personality traits,preferences, and the like. For example, if the allocation engine 105determines that a person engages in sky diving, parachuting, etc., theallocation engine 105 may determine that the user does not have a fearof heights, and computes a score reflecting a high degree of suitabilityfor allocating the user to an exit row seat.

At block 370, the allocation engine 105 generates an ordered list ofcandidate passengers based on the determined attributes and/or thescores computed for each passenger. Generally, the allocation engine 105orders the list based on one or more scores computed for each passenger.However, if the allocation engine 105 determines a passenger attributeviolates a rule, the allocation engine 105 may place the passenger atthe bottom of the list. At block 280, described in greater detail withreference to FIG. 7, the allocation engine 105 allocates passengers toseats based at least in part on the ordered list of passengers. Theallocation engine 105 may store the results of the allocation in theallocation data 108, which may then be transmitted to the requestingentity, or may otherwise be made available to the requesting entity.

FIG. 4 is a flow chart illustrating an example method 400 correspondingto block 350 to determine physical attributes of passengers, accordingto one embodiment. As shown, the method 400 begins at block 410, wherethe allocation engine 105 executes a loop including blocks 420-480 foreach passenger that was not filtered at block 340. At block 420, theallocation engine 105 receives data from the data sources 104 for thecurrent passenger, such as profile data from the profiles 115, socialmedia data 113, and publication data 114. At block 430, the allocationengine 105 optionally extracts attributes from the data received atblock 420. For example, the allocation engine 105 may apply naturallanguage processing to blog posts generated by the current passenger toextract sentiment, emotions, preferences, and other attributestherefrom. In such an example, the allocation 105 may determine that theblog posts indicate the passenger exercises multiple times per day, andis therefore likely physically fit. As another example, the allocationengine 105 may analyze images of the current passenger to determine thepassenger is physically fit, images shared by the current passenger, andcontent “liked” by the passenger on social media platforms to extractattributes of the passenger therefrom.

At block 440, the allocation engine 105 makes an API call to thephysical attribute service 112. The API call may include an indicationof the current passenger, such as the passenger's full name, a useraccount name for social media services, email addresses extracted fromthe profile 115, and the like. At block 450, the allocation engine 105receives a result set from the physical attribute service 112. Theresult set specifies one or more attributes determined based on analysisof images and/or other data describing the passenger. For example, theresult set may specify physical height, weight, whether the passenger isphysically fit, whether the passenger has significant muscle mass, andthe like. At block 460, the allocation engine 105 computes a physicalattribute score for the user based on the weights specified in the MLmodels 106. At block 470, the allocation engine 105 removes the currentpassenger as a candidate for seating in a seat upon determining that atleast one of the determined physical attributes of the passenger do notsatisfy at least one rule for the seat in the rules 107. For example, ifthe physical attribute score of the passenger does not exceed a minimumphysical attribute score for a seat, the allocation engine 105 removesthe current passenger from consideration for allocation for the seat. Atblock 480, the allocation engine 105 determines whether more passengersremain. If more passengers remain, the method returns to block 410.Otherwise, the method 400 ends.

FIG. 5 is a flow chart illustrating an example method 500 correspondingto block 360 to determine non-physical attributes of passengers,according to one embodiment. As shown, the method 500 begins at block510, where the allocation engine 105 executes a loop including blocks520-580 for each passenger that was not filtered at block 340. At block520, the allocation engine 105 receives data from the data sources 104for the current passenger, such as profile data from the profiles 115,social media data 113, and publication data 114. At block 530, theallocation engine 105 optionally extracts attributes from the datareceived at block 520. For example, the allocation engine 105 may applynatural language processing to social media posts generated by thecurrent passenger to extract sentiment, emotions, preferences, and otherattributes therefrom. For example, if the allocation engine 105 detectsa fearful or negative sentiment relative to heights in the social mediaposts, the allocation engine 105 may determine that the user has a fearof flying.

At block 540, the allocation engine 105 makes an API call to thenon-physical attribute service 111. The API call may include anindication of the current passenger, such as the passenger's full name,a user account name for social media services, email addresses extractedfrom the profile 115, and the like. At block 550, the allocation engine105 receives a result set from the non-physical attribute service 111.The result set specifies one or more non-physical attributes of thepassenger, such as mental state, willingness to help others, personalitytraits, fears, likes, dislikes, emotions, and the like. At block 560,the allocation engine 105 computes a non-physical attribute score forthe user based on the weights specified in the ML models 106. At block570, the allocation engine 105 optionally computes a composite scorebased on the ML models 106 and the physical and non-physical attributescores computed for the passenger at blocks 460 and 560, respectively.Doing so allows the allocation engine 105 to consider both the physicaland non-physical attributes of the passenger when allocating thepassenger to a seat. At block 580, the allocation engine 105 removes thecurrent passenger as a candidate for seating in a seat upon determiningthat at least one of the determined physical attributes of the passengerdo not satisfy at least one rule for the seat in the rules 107. Forexample, if the passenger expresses the emotions of fear, and a negativesentiment towards helping others, the allocation engine 105 may removethe passenger from consideration for allocation to a seat associatedwith the violated rule(s). As another example, if the non-physicalattribute score does not exceed a minimum non-physical attribute scorethreshold, the allocation engine 105 may remove the passenger fromconsideration for allocation to a seat associated with the violatedrule. Similarly, if the composite score does not exceed a minimumcomposite score threshold, the allocation engine 105 may remove thepassenger from consideration for allocation to a seat associated withthe violated rule. At block 590, the allocation engine 105 determineswhether more passengers remain. If more passengers remain, the methodreturns to block 510. Otherwise, the method 500 ends.

FIG. 6 is a flow chart illustrating an example method 600 correspondingto block 380 to allocate passengers to seats in a vehicle based onordered list of candidate passengers, according to one embodiment. Asshown, the method 600 begins at block 610, where the allocation engine105 generates an ordered list of seats based on the seat priority foreach vehicle specified in the inventory data 110. In one embodiment, theordered list of seats is received with the inventory data 110. At block620, the allocation engine 105 executes a loop including blocks 630-670for each seat in the ordered list. At block 630, the allocation engine105 determines the rules from the rules 107 that are applicable to thecurrent seat. For example, the current seat may be associated with rulesthat require the passenger to not be afraid of heights, express apositive sentiment towards helping others, required physical attributes,and the like.

At block 640, the allocation engine 105 receives the highest rankedpassenger from the ordered list of passengers generated at block 370. Atblock 650, the allocation engine 105 allocates the highest rankedpassenger to the current seat, e.g., by storing an indication of theallocation in the allocation data 108. At block 660, the allocationengine 105 removes the passenger allocated to the seat from the orderedlist of passengers. At block 670, the allocation engine 105 determineswhether more seats remain. If more seats remain, the allocation engine105 returns to block 670. Otherwise, the method 600 ends.

FIG. 7 illustrates an example system 700 which provides cognitive-basedpassenger selection for vehicles, according to one embodiment. Thenetworked system 700 includes a server 101. The server 101 may also beconnected to other computers (e.g., the inventory sources 102, theservices 103, and the data sources 104) via the network 130. In general,the network 130 may be a telecommunications network and/or a wide areanetwork (WAN). In a particular embodiment, the network 130 is theInternet.

The server 101 generally includes a processor 704 which obtainsinstructions and data via a bus 720 from a memory 706 and/or a storage708. The server 101 may also include one or more network interfacedevices 718, input devices 722, and output devices 724 connected to thebus 720. The server 101 is generally under the control of an operatingsystem (not shown). Examples of operating systems include the UNIXoperating system, versions of the Microsoft Windows operating system,and distributions of the Linux operating system. (UNIX is a registeredtrademark of The Open Group in the United States and other countries.Microsoft and Windows are trademarks of Microsoft Corporation in theUnited States, other countries, or both. Linux is a registered trademarkof Linus Torvalds in the United States, other countries, or both.) Moregenerally, any operating system supporting the functions disclosedherein may be used. The processor 704 is a programmable logic devicethat performs instruction, logic, and mathematical processing, and maybe representative of one or more CPUs. The network interface device 718may be any type of network communications device allowing the server 101to communicate with other computers via the network 130.

The storage 708 is representative of hard-disk drives, solid statedrives, flash memory devices, optical media and the like. Generally, thestorage 708 stores application programs and data for use by the server101. In addition, the memory 706 and the storage 708 may be consideredto include memory physically located elsewhere; for example, on anothercomputer coupled to the server 101 via the bus 720.

The input device 722 may be any device for providing input to the server101. For example, a keyboard and/or a mouse may be used. The inputdevice 722 represents a wide variety of input devices, includingkeyboards, mice, controllers, and so on. Furthermore, the input device722 may include a set of buttons, switches or other physical devicemechanisms for controlling the server 101. The output device 724 mayinclude output devices such as monitors, touch screen displays, and soon.

As shown, the memory 706 contains the allocation engine 105, while thestorage 708 contains the ML models 106, rules 107, allocation data 108,and training data 109, each described in greater detail above.Generally, the system 700 implements all systems, methods, andfunctionality described above with reference to FIGS. 1-6.

Advantageously, embodiments disclosed herein provide techniques tointelligently allocate passengers to seats in mass-transit vehicles. Byidentifying those persons most willing and able to sit in a particularseat, the safety of all passengers is improved. Furthermore, performanceof the server 101 is improved, as the allocation engine 105 is able togenerate more accurate allocations that need not be revised. Doing sosaves computing resources of the server 101.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

In the foregoing, reference is made to embodiments presented in thisdisclosure. However, the scope of the present disclosure is not limitedto specific described embodiments. Instead, any combination of therecited features and elements, whether related to different embodimentsor not, is contemplated to implement and practice contemplatedembodiments. Furthermore, although embodiments disclosed herein mayachieve advantages over other possible solutions or over the prior art,whether or not a particular advantage is achieved by a given embodimentis not limiting of the scope of the present disclosure. Thus, therecited aspects, features, embodiments and advantages are merelyillustrative and are not considered elements or limitations of theappended claims except where explicitly recited in a claim(s). Likewise,reference to “the invention” shall not be construed as a generalizationof any inventive subject matter disclosed herein and shall not beconsidered to be an element or limitation of the appended claims exceptwhere explicitly recited in a claim(s).

Aspects of the present invention may take the form of an entirelyhardware embodiment, an entirely software embodiment (includingfirmware, resident software, micro-code, etc.) or an embodimentcombining software and hardware aspects that may all generally bereferred to herein as a “circuit,” “module” or “system.”

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Embodiments of the invention may be provided to end users through acloud computing infrastructure. Cloud computing generally refers to theprovision of scalable computing resources as a service over a network.More formally, cloud computing may be defined as a computing capabilitythat provides an abstraction between the computing resource and itsunderlying technical architecture (e.g., servers, storage, networks),enabling convenient, on-demand network access to a shared pool ofconfigurable computing resources that can be rapidly provisioned andreleased with minimal management effort or service provider interaction.Thus, cloud computing allows a user to access virtual computingresources (e.g., storage, data, applications, and even completevirtualized computing systems) in “the cloud,” without regard for theunderlying physical systems (or locations of those systems) used toprovide the computing resources.

Typically, cloud computing resources are provided to a user on apay-per-use basis, where users are charged only for the computingresources actually used (e.g. an amount of storage space consumed by auser or a number of virtualized systems instantiated by the user). Auser can access any of the resources that reside in the cloud at anytime, and from anywhere across the Internet. In context of the presentinvention, a user may access applications or related data available inthe cloud. For example, the allocation engine 105 could execute on acomputing system in the cloud. In such a case, the allocation engine 105may store the seat allocations in the allocation data 108 for aplurality of clients at a storage location in the cloud. Doing so allowsa user to access this information from any computing system attached toa network connected to the cloud (e.g., the Internet).

While the foregoing is directed to embodiments of the present invention,other and further embodiments of the invention may be devised withoutdeparting from the basic scope thereof, and the scope thereof isdetermined by the claims that follow.

What is claimed is:
 1. A method, comprising: receiving, by anapplication executing on a processor, a plurality of rules associatedwith a first seat of a plurality of seats in a mass-transit vehicle;determining a plurality of physical attributes for a first passenger, ofa plurality of passengers, based on data describing the first passengerreceived from a plurality of data sources; determining a plurality ofnon-physical attributes for the first passenger based on the receiveddata describing the first passenger; determining that at least one ofthe plurality of physical attributes and the plurality of non-physicalattributes of the first passenger satisfy each rule in the set of rulesassociated with the first seat; computing, based on a machine learning(ML) model a score for the first passenger; determining that the scoreexceeds a threshold score associated with the first seat; andallocating, by the application, the first passenger to the first seat inthe mass-transit vehicle.
 2. The method of claim 1, wherein themass-transit vehicle comprises one of: (i) an airplane, (ii) a bus, and(iii) a train, wherein a first physical attribute of the plurality ofphysical attributes is received from a physical attribute service via anetwork, wherein the physical attribute service determines the firstphysical attribute based on an analysis of an image of the firstpassenger received from at least one of the plurality of data sources.3. The method of claim 1, wherein the non-physical attributes comprise:(i) emotions expressed by the first passenger, (ii) sentiment expressedby the first passenger, (iii) a preference of the first passenger, and(iv) a personality trait of the passenger, wherein a first non-physicalattribute of the plurality of non-physical attributes is received from anon-physical attribute service via a network, wherein the non-physicalattribute service determines the first non-physical attribute based onan analysis of: (i) blog posts generated by the first passenger, (ii)social media publications generated by the first passenger, and (iii) aprofile of the first passenger received from the plurality of datasources.
 4. The method of claim 1, further comprising: training theapplication to generate the ML model based on training data, wherein theML model specifies a respective weight for the plurality of physicalattributes and the plurality of non-physical attributes, wherein theapplication computes the score based on the weights specified in the MLmodel.
 5. The method of claim 4, wherein the score comprises a compositescore, wherein the composite score is computed based on: (i) a physicalattribute score computed based on the weights specified in the ML modeland the plurality of determined physical attributes of the firstpassenger, (ii) a non-physical attribute score computed based on theweights specified in the ML model and the plurality of determinednon-physical attributes of the first passenger.
 6. The method of claim1, further comprising: computing a respective score for each of theplurality of passengers based on the ML model and a respective pluralityof physical and non-physical attributes of each passenger; generating anordered list of the plurality of passengers ranked based on the computedscore for each passenger; and allocating each of the plurality ofpassengers to a respective seat in the vehicle based on the ordered listof passengers.
 7. The method of claim 1, further comprising: determininga plurality of physical attributes for a second passenger, of theplurality of passengers, based on data describing the second passengerreceived from the plurality of data sources; determining a plurality ofnon-physical attributes for the second passenger based on the receiveddata describing the first passenger; determining that at least one ofthe plurality of physical attributes and the plurality of non-physicalattributes of the second passenger violate at least one rule associatedwith a second seat in the vehicle; denoting, in a record stored in amemory, that the second seat in the vehicle is unavailable to the secondpassenger based on the violation of the at least one rule associatedwith the second seat; and allocating the second passenger to a thirdseat in the vehicle.
 8. A computer program product, comprising: anon-transitory computer-readable storage medium having computer readableprogram code embodied therewith, the computer readable program codeexecutable by a processor to perform an operation comprising: receivinga plurality of rules associated with a first seat of a plurality ofseats in a mass-transit vehicle; determining a plurality of physicalattributes for a first passenger, of a plurality of passengers, based ondata describing the first passenger received from a plurality of datasources; determining a plurality of non-physical attributes for thefirst passenger based on the received data describing the firstpassenger; determining that at least one of the plurality of physicalattributes and the plurality of non-physical attributes of the firstpassenger satisfy each rule in the set of rules associated with thefirst seat; computing, based on a machine learning (ML) model a scorefor the first passenger; determining that the score exceeds a thresholdscore associated with the first seat; and allocating the first passengerto the first seat in the mass-transit vehicle.
 9. The computer programproduct of claim 8, wherein the mass-transit vehicle comprises one of:(i) an airplane, (ii) a bus, and (iii) a train, wherein a first physicalattribute of the plurality of physical attributes is received from aphysical attribute service via a network, wherein the physical attributeservice determines the first physical attribute based on an analysis ofan image of the first passenger received from at least one of theplurality of data sources.
 10. The computer program product of claim 8,wherein the non-physical attributes comprise: (i) emotions expressed bythe first passenger, (ii) sentiment expressed by the first passenger,(iii) a preference of the first passenger, and (iv) a personality traitof the passenger, wherein a first non-physical attribute of theplurality of non-physical attributes is received from a non-physicalattribute service via a network, wherein the non-physical attributeservice determines the first non-physical attribute based on an analysisof: (i) blog posts generated by the first passenger, (ii) social mediapublications generated by the first passenger, and (iii) a profile ofthe first passenger received from the plurality of data sources.
 11. Thecomputer program product of claim 8, the operation further comprising:training the application to generate the ML model based on trainingdata, wherein the ML model specifies a respective weight for theplurality of physical attributes and the plurality of non-physicalattributes, wherein the application computes the score based on theweights specified in the ML model.
 12. The computer program product ofclaim 11, wherein the score comprises a composite score, wherein thecomposite score is computed based on: (i) a physical attribute scorecomputed based on the weights specified in the ML model and theplurality of determined physical attributes of the first passenger, (ii)a non-physical attribute score computed based on the weights specifiedin the ML model and the plurality of determined non-physical attributesof the first passenger.
 13. The computer program product of claim 8, theoperation further comprising: computing a respective score for each ofthe plurality of passengers based on the ML model and a respectiveplurality of physical and non-physical attributes of each passenger;generating an ordered list of the plurality of passengers ranked basedon the computed score for each passenger; and allocating each of theplurality of passengers to a respective seat in the vehicle based on theordered list of passengers.
 14. The computer program product of claim 8,the operation further comprising: determining a plurality of physicalattributes for a second passenger, of the plurality of passengers, basedon data describing the second passenger received from the plurality ofdata sources; determining a plurality of non-physical attributes for thesecond passenger based on the received data describing the firstpassenger; determining that at least one of the plurality of physicalattributes and the plurality of non-physical attributes of the secondpassenger violate at least one rule associated with a second seat in thevehicle; denoting, in a record stored in a memory, that the second seatin the vehicle is unavailable to the second passenger based on theviolation of the at least one rule associated with the second seat; andallocating the second passenger to a third seat in the vehicle.
 15. Asystem, comprising: a processor; and a memory storing one or moreinstructions which, when executed by the processor, performs anoperation comprising: receiving a plurality of rules associated with afirst seat of a plurality of seats in a mass-transit vehicle;determining a plurality of physical attributes for a first passenger, ofa plurality of passengers, based on data describing the first passengerreceived from a plurality of data sources; determining a plurality ofnon-physical attributes for the first passenger based on the receiveddata describing the first passenger; determining that at least one ofthe plurality of physical attributes and the plurality of non-physicalattributes of the first passenger satisfy each rule in the set of rulesassociated with the first seat; computing, based on a machine learning(ML) model a score for the first passenger; determining that the scoreexceeds a threshold score associated with the first seat; and allocatingthe first passenger to the first seat in the mass-transit vehicle. 16.The system of claim 15, wherein the mass-transit vehicle comprises oneof: (i) an airplane, (ii) a bus, and (iii) a train, wherein a firstphysical attribute of the plurality of physical attributes is receivedfrom a physical attribute service via a network, wherein the physicalattribute service determines the first physical attribute based on ananalysis of an image of the first passenger received from at least oneof the plurality of data sources.
 17. The system of claim 15, whereinthe non-physical attributes comprise: (i) emotions expressed by thefirst passenger, (ii) sentiment expressed by the first passenger, (iii)a preference of the first passenger, and (iv) a personality trait of thepassenger, wherein a first non-physical attribute of the plurality ofnon-physical attributes is received from a non-physical attributeservice via a network, wherein the non-physical attribute servicedetermines the first non-physical attribute based on an analysis of: (i)blog posts generated by the first passenger, (ii) social mediapublications generated by the first passenger, and (iii) a profile ofthe first passenger received from the plurality of data sources.
 18. Thesystem of claim 17, wherein the score comprises a composite score,wherein the composite score is computed based on: (i) a physicalattribute score computed based on the weights specified in the ML modeland the plurality of determined physical attributes of the firstpassenger, (ii) a non-physical attribute score computed based on theweights specified in the ML model and the plurality of determinednon-physical attributes of the first passenger.
 19. The system of claim15, the operation further comprising: computing a respective score foreach of the plurality of passengers based on the ML model and arespective plurality of physical and non-physical attributes of eachpassenger; generating an ordered list of the plurality of passengersranked based on the computed score for each passenger; and allocatingeach of the plurality of passengers to a respective seat in the vehiclebased on the ordered list of passengers.
 20. The system of claim 15, theoperation further comprising: determining a plurality of physicalattributes for a second passenger, of the plurality of passengers, basedon data describing the second passenger received from the plurality ofdata sources; determining a plurality of non-physical attributes for thesecond passenger based on the received data describing the firstpassenger; determining that at least one of the plurality of physicalattributes and the plurality of non-physical attributes of the secondpassenger violate at least one rule associated with a second seat in thevehicle; denoting, in a record stored in the memory, that the secondseat in the vehicle is unavailable to the second passenger based on theviolation of the at least one rule associated with the second seat; andallocating the second passenger to a third seat in the vehicle.